SEM mediation modeling for neighbourhood environment

This is a Rmarkdown file documented the mediation model for neighbourhood envrionment mediate the prediction from 6 polygenic scores towards academic achievement across compulsory education (age 7, 9, 12, 16)

load the packages and dataset

library(lavaan)

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library(psych)

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library(psychometric)

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require(mediation)

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library(devtools)

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library(semPlot)
library(tidySEM)

dat <- read.csv(file = "env_mediation_prepped_bothtwins_random1_twin1and2_genotyped.csv", header = T)

Select variables

##### 23 academic outcomes measures #####

EA_all=c("gte1", # English achievement composite (7 year teacher), standardised"
"gtm1", # Math achievement composite (7 year teacher), standardised"
"gta1", # overall achievement composite (7 year teacher), standardised"
"ite1",
"itm1",
"its1",
"it3a1", # "3-subject overall academic achievement composite (9 year teacher), standardised"
"it2a1", #
"lte1",
"ltm1",
"lts1",
"lt2a1", # "2 (english and math) -subject overall academic achievement composite (12 year teacher), standardised"
"lt3a1", # # "3-subject overall academic achievement composite (12 year teacher), standardised"

      "pcexgcsenum1",
      
      # number of GCSE passes, at A grade
      "pcexgcseabcnum1",
      
      # point score total for gcse passe
      "pcexgcsepsct1",
      
      "pcexgcsegrdm1",
      "pcexothergrdm1",
      "pcexadvgrdm1",
      "pcexallgrdm1",
      "pcexgcseengm1",
      "pcexgcsescim1",
      "pcexgcsematm1",
      "pcexgcsecorem1")



##### 6 PGS  #####

GPS1=c("EA3_Lee2018_no23andme_corrected_FRCT1", "IQ_Savage2018_corrected_FRCT1",
       "c_chol_cog_noncog_corrected_FRC_1", "nc_chol_cog_noncog_corrected_FRC_1",
       "Cog_corrected_FRC_1", "NonCog_corrected_FRC_1")



#### 6 neighbourhoods environment mediators ######

Mediator_nei=c(

  # ========== Pollution ==========
  ## Mean composites
"pollution",
  
  
  
  # ========== Neighborhood quality ==========
  ## Mean composites
"neighbourhood_quality_occupancyrating",
"neighbourhood_quality_healthrating",
"neighbourhood_quality_householdsize",
"neighbourhood_quality_populinhouseholds",
  
  
  
  # ========== Neighborhood economy ==========
  ## Mean composites
"neighbourhood_economy_qualification"
)



### 828 mediation models intotal ###

1 Model specifying

1.1 Specify EA models

EA3_Lee2018_no23andme_corrected_FRCT1_pollution_gte1 <- " 
 #path c (direct effect) 
 gte1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gte1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_gte1 <- " 
 #path c (direct effect) 
 gte1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gte1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_gte1 <- " 
 #path c (direct effect) 
 gte1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gte1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_gte1 <- " 
 #path c (direct effect) 
 gte1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gte1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_gte1 <- " 
 #path c (direct effect) 
 gte1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gte1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_gte1 <- " 
 #path c (direct effect) 
 gte1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gte1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gtm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gtm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gtm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gtm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gtm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gtm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_gta1 <- " 
 #path c (direct effect) 
 gta1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gta1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_gta1 <- " 
 #path c (direct effect) 
 gta1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gta1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_gta1 <- " 
 #path c (direct effect) 
 gta1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gta1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_gta1 <- " 
 #path c (direct effect) 
 gta1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gta1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_gta1 <- " 
 #path c (direct effect) 
 gta1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gta1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_gta1 <- " 
 #path c (direct effect) 
 gta1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 gta1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_ite1 <- " 
 #path c (direct effect) 
 ite1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 ite1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_ite1 <- " 
 #path c (direct effect) 
 ite1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 ite1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_ite1 <- " 
 #path c (direct effect) 
 ite1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 ite1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_ite1 <- " 
 #path c (direct effect) 
 ite1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 ite1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_ite1 <- " 
 #path c (direct effect) 
 ite1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 ite1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_ite1 <- " 
 #path c (direct effect) 
 ite1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 ite1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_itm1 <- " 
 #path c (direct effect) 
 itm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 itm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_itm1 <- " 
 #path c (direct effect) 
 itm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 itm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_itm1 <- " 
 #path c (direct effect) 
 itm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 itm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_itm1 <- " 
 #path c (direct effect) 
 itm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 itm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_itm1 <- " 
 #path c (direct effect) 
 itm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 itm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_itm1 <- " 
 #path c (direct effect) 
 itm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 itm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_its1 <- " 
 #path c (direct effect) 
 its1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 its1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_its1 <- " 
 #path c (direct effect) 
 its1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 its1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_its1 <- " 
 #path c (direct effect) 
 its1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 its1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_its1 <- " 
 #path c (direct effect) 
 its1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 its1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_its1 <- " 
 #path c (direct effect) 
 its1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 its1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_its1 <- " 
 #path c (direct effect) 
 its1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 its1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 it3a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 it3a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 it3a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 it3a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 it3a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 it3a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 it2a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 it2a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 it2a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 it2a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 it2a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 it2a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_lte1 <- " 
 #path c (direct effect) 
 lte1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lte1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_lte1 <- " 
 #path c (direct effect) 
 lte1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lte1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_lte1 <- " 
 #path c (direct effect) 
 lte1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lte1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_lte1 <- " 
 #path c (direct effect) 
 lte1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lte1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_lte1 <- " 
 #path c (direct effect) 
 lte1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lte1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_lte1 <- " 
 #path c (direct effect) 
 lte1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lte1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 ltm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 ltm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 ltm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 ltm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 ltm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 ltm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_lts1 <- " 
 #path c (direct effect) 
 lts1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lts1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_lts1 <- " 
 #path c (direct effect) 
 lts1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lts1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_lts1 <- " 
 #path c (direct effect) 
 lts1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lts1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_lts1 <- " 
 #path c (direct effect) 
 lts1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lts1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_lts1 <- " 
 #path c (direct effect) 
 lts1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lts1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_lts1 <- " 
 #path c (direct effect) 
 lts1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lts1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lt3a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lt3a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lt3a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lt3a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lt3a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lt3a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lt2a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lt2a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lt2a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lt2a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lt2a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 lt2a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsenum1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsenum1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcseabcnum1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsepsct1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsepsct1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsegrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexothergrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexothergrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexadvgrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexadvgrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexallgrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexallgrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcseengm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcseengm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsescim1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsescim1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsematm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsematm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 pollution~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsecorem1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*EA3_Lee2018_no23andme_corrected_FRCT1 
 # path b
 pcexgcsecorem1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "

1.2 Specify IQ models

IQ_Savage2018_corrected_FRCT1_pollution_gte1 <- " 
 #path c (direct effect) 
 gte1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gte1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_gte1 <- " 
 #path c (direct effect) 
 gte1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gte1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_gte1 <- " 
 #path c (direct effect) 
 gte1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gte1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_gte1 <- " 
 #path c (direct effect) 
 gte1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gte1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_gte1 <- " 
 #path c (direct effect) 
 gte1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gte1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_gte1 <- " 
 #path c (direct effect) 
 gte1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gte1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gtm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gtm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gtm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gtm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gtm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gtm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_gta1 <- " 
 #path c (direct effect) 
 gta1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gta1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_gta1 <- " 
 #path c (direct effect) 
 gta1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gta1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_gta1 <- " 
 #path c (direct effect) 
 gta1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gta1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_gta1 <- " 
 #path c (direct effect) 
 gta1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gta1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_gta1 <- " 
 #path c (direct effect) 
 gta1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gta1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_gta1 <- " 
 #path c (direct effect) 
 gta1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 gta1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_ite1 <- " 
 #path c (direct effect) 
 ite1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 ite1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_ite1 <- " 
 #path c (direct effect) 
 ite1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 ite1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_ite1 <- " 
 #path c (direct effect) 
 ite1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 ite1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_ite1 <- " 
 #path c (direct effect) 
 ite1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 ite1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_ite1 <- " 
 #path c (direct effect) 
 ite1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 ite1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_ite1 <- " 
 #path c (direct effect) 
 ite1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 ite1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_itm1 <- " 
 #path c (direct effect) 
 itm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 itm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_itm1 <- " 
 #path c (direct effect) 
 itm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 itm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_itm1 <- " 
 #path c (direct effect) 
 itm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 itm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_itm1 <- " 
 #path c (direct effect) 
 itm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 itm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_itm1 <- " 
 #path c (direct effect) 
 itm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 itm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_itm1 <- " 
 #path c (direct effect) 
 itm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 itm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_its1 <- " 
 #path c (direct effect) 
 its1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 its1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_its1 <- " 
 #path c (direct effect) 
 its1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 its1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_its1 <- " 
 #path c (direct effect) 
 its1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 its1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_its1 <- " 
 #path c (direct effect) 
 its1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 its1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_its1 <- " 
 #path c (direct effect) 
 its1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 its1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_its1 <- " 
 #path c (direct effect) 
 its1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 its1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 it3a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 it3a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 it3a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 it3a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 it3a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 it3a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 it2a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 it2a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 it2a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 it2a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 it2a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 it2a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_lte1 <- " 
 #path c (direct effect) 
 lte1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lte1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_lte1 <- " 
 #path c (direct effect) 
 lte1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lte1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_lte1 <- " 
 #path c (direct effect) 
 lte1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lte1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_lte1 <- " 
 #path c (direct effect) 
 lte1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lte1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_lte1 <- " 
 #path c (direct effect) 
 lte1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lte1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_lte1 <- " 
 #path c (direct effect) 
 lte1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lte1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 ltm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 ltm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 ltm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 ltm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 ltm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 ltm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_lts1 <- " 
 #path c (direct effect) 
 lts1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lts1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_lts1 <- " 
 #path c (direct effect) 
 lts1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lts1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_lts1 <- " 
 #path c (direct effect) 
 lts1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lts1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_lts1 <- " 
 #path c (direct effect) 
 lts1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lts1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_lts1 <- " 
 #path c (direct effect) 
 lts1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lts1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_lts1 <- " 
 #path c (direct effect) 
 lts1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lts1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lt3a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lt3a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lt3a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lt3a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lt3a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lt3a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lt2a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lt2a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lt2a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lt2a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lt2a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 lt2a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsenum1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsenum1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcseabcnum1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsepsct1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsepsct1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsegrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexothergrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexothergrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexadvgrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexadvgrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexallgrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexallgrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcseengm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcseengm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsescim1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsescim1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsematm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsematm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 pollution~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsecorem1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_occupancyrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_healthrating~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_householdsize~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_quality_populinhouseholds~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*IQ_Savage2018_corrected_FRCT1 
 # path a
 neighbourhood_economy_qualification~a*IQ_Savage2018_corrected_FRCT1 
 # path b
 pcexgcsecorem1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "

1.3 Specify Cognitive_extended models

c_chol_cog_noncog_corrected_FRC_1_pollution_gte1 <- " 
 #path c (direct effect) 
 gte1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gte1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_gte1 <- " 
 #path c (direct effect) 
 gte1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_gte1 <- " 
 #path c (direct effect) 
 gte1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_gte1 <- " 
 #path c (direct effect) 
 gte1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_gte1 <- " 
 #path c (direct effect) 
 gte1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_gte1 <- " 
 #path c (direct effect) 
 gte1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gtm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_gta1 <- " 
 #path c (direct effect) 
 gta1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gta1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_gta1 <- " 
 #path c (direct effect) 
 gta1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_gta1 <- " 
 #path c (direct effect) 
 gta1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_gta1 <- " 
 #path c (direct effect) 
 gta1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_gta1 <- " 
 #path c (direct effect) 
 gta1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_gta1 <- " 
 #path c (direct effect) 
 gta1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_ite1 <- " 
 #path c (direct effect) 
 ite1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 ite1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_ite1 <- " 
 #path c (direct effect) 
 ite1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_ite1 <- " 
 #path c (direct effect) 
 ite1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_ite1 <- " 
 #path c (direct effect) 
 ite1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_ite1 <- " 
 #path c (direct effect) 
 ite1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_ite1 <- " 
 #path c (direct effect) 
 ite1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_itm1 <- " 
 #path c (direct effect) 
 itm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 itm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_itm1 <- " 
 #path c (direct effect) 
 itm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_itm1 <- " 
 #path c (direct effect) 
 itm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_itm1 <- " 
 #path c (direct effect) 
 itm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_itm1 <- " 
 #path c (direct effect) 
 itm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_itm1 <- " 
 #path c (direct effect) 
 itm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_its1 <- " 
 #path c (direct effect) 
 its1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 its1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_its1 <- " 
 #path c (direct effect) 
 its1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_its1 <- " 
 #path c (direct effect) 
 its1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_its1 <- " 
 #path c (direct effect) 
 its1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_its1 <- " 
 #path c (direct effect) 
 its1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_its1 <- " 
 #path c (direct effect) 
 its1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 it3a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 it2a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_lte1 <- " 
 #path c (direct effect) 
 lte1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lte1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lte1 <- " 
 #path c (direct effect) 
 lte1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_lte1 <- " 
 #path c (direct effect) 
 lte1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_lte1 <- " 
 #path c (direct effect) 
 lte1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lte1 <- " 
 #path c (direct effect) 
 lte1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_lte1 <- " 
 #path c (direct effect) 
 lte1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 ltm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_lts1 <- " 
 #path c (direct effect) 
 lts1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lts1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lts1 <- " 
 #path c (direct effect) 
 lts1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_lts1 <- " 
 #path c (direct effect) 
 lts1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_lts1 <- " 
 #path c (direct effect) 
 lts1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lts1 <- " 
 #path c (direct effect) 
 lts1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_lts1 <- " 
 #path c (direct effect) 
 lts1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt3a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt2a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*c_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*c_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "

1.4 Specify Noncognitive_extended models

nc_chol_cog_noncog_corrected_FRC_1_pollution_gte1 <- " 
 #path c (direct effect) 
 gte1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gte1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_gte1 <- " 
 #path c (direct effect) 
 gte1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_gte1 <- " 
 #path c (direct effect) 
 gte1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_gte1 <- " 
 #path c (direct effect) 
 gte1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_gte1 <- " 
 #path c (direct effect) 
 gte1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_gte1 <- " 
 #path c (direct effect) 
 gte1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gtm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_gta1 <- " 
 #path c (direct effect) 
 gta1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gta1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_gta1 <- " 
 #path c (direct effect) 
 gta1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_gta1 <- " 
 #path c (direct effect) 
 gta1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_gta1 <- " 
 #path c (direct effect) 
 gta1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_gta1 <- " 
 #path c (direct effect) 
 gta1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_gta1 <- " 
 #path c (direct effect) 
 gta1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_ite1 <- " 
 #path c (direct effect) 
 ite1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 ite1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_ite1 <- " 
 #path c (direct effect) 
 ite1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_ite1 <- " 
 #path c (direct effect) 
 ite1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_ite1 <- " 
 #path c (direct effect) 
 ite1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_ite1 <- " 
 #path c (direct effect) 
 ite1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_ite1 <- " 
 #path c (direct effect) 
 ite1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_itm1 <- " 
 #path c (direct effect) 
 itm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 itm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_itm1 <- " 
 #path c (direct effect) 
 itm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_itm1 <- " 
 #path c (direct effect) 
 itm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_itm1 <- " 
 #path c (direct effect) 
 itm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_itm1 <- " 
 #path c (direct effect) 
 itm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_itm1 <- " 
 #path c (direct effect) 
 itm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_its1 <- " 
 #path c (direct effect) 
 its1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 its1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_its1 <- " 
 #path c (direct effect) 
 its1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_its1 <- " 
 #path c (direct effect) 
 its1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_its1 <- " 
 #path c (direct effect) 
 its1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_its1 <- " 
 #path c (direct effect) 
 its1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_its1 <- " 
 #path c (direct effect) 
 its1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 it3a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 it2a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_lte1 <- " 
 #path c (direct effect) 
 lte1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lte1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lte1 <- " 
 #path c (direct effect) 
 lte1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_lte1 <- " 
 #path c (direct effect) 
 lte1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_lte1 <- " 
 #path c (direct effect) 
 lte1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lte1 <- " 
 #path c (direct effect) 
 lte1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_lte1 <- " 
 #path c (direct effect) 
 lte1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 ltm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_lts1 <- " 
 #path c (direct effect) 
 lts1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lts1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lts1 <- " 
 #path c (direct effect) 
 lts1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_lts1 <- " 
 #path c (direct effect) 
 lts1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_lts1 <- " 
 #path c (direct effect) 
 lts1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lts1 <- " 
 #path c (direct effect) 
 lts1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_lts1 <- " 
 #path c (direct effect) 
 lts1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt3a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt2a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 pollution~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*nc_chol_cog_noncog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*nc_chol_cog_noncog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "

1.5 Specify Cognitive_previous models

Cog_corrected_FRC_1_pollution_gte1 <- " 
 #path c (direct effect) 
 gte1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 gte1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_gte1 <- " 
 #path c (direct effect) 
 gte1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_gte1 <- " 
 #path c (direct effect) 
 gte1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_gte1 <- " 
 #path c (direct effect) 
 gte1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_gte1 <- " 
 #path c (direct effect) 
 gte1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_gte1 <- " 
 #path c (direct effect) 
 gte1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 gtm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_gta1 <- " 
 #path c (direct effect) 
 gta1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 gta1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_gta1 <- " 
 #path c (direct effect) 
 gta1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_gta1 <- " 
 #path c (direct effect) 
 gta1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_gta1 <- " 
 #path c (direct effect) 
 gta1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_gta1 <- " 
 #path c (direct effect) 
 gta1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_gta1 <- " 
 #path c (direct effect) 
 gta1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_ite1 <- " 
 #path c (direct effect) 
 ite1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 ite1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_ite1 <- " 
 #path c (direct effect) 
 ite1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_ite1 <- " 
 #path c (direct effect) 
 ite1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_ite1 <- " 
 #path c (direct effect) 
 ite1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_ite1 <- " 
 #path c (direct effect) 
 ite1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_ite1 <- " 
 #path c (direct effect) 
 ite1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_itm1 <- " 
 #path c (direct effect) 
 itm1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 itm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_itm1 <- " 
 #path c (direct effect) 
 itm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_itm1 <- " 
 #path c (direct effect) 
 itm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_itm1 <- " 
 #path c (direct effect) 
 itm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_itm1 <- " 
 #path c (direct effect) 
 itm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_itm1 <- " 
 #path c (direct effect) 
 itm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_its1 <- " 
 #path c (direct effect) 
 its1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 its1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_its1 <- " 
 #path c (direct effect) 
 its1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_its1 <- " 
 #path c (direct effect) 
 its1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_its1 <- " 
 #path c (direct effect) 
 its1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_its1 <- " 
 #path c (direct effect) 
 its1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_its1 <- " 
 #path c (direct effect) 
 its1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 it3a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 it2a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_lte1 <- " 
 #path c (direct effect) 
 lte1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 lte1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lte1 <- " 
 #path c (direct effect) 
 lte1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_lte1 <- " 
 #path c (direct effect) 
 lte1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_lte1 <- " 
 #path c (direct effect) 
 lte1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lte1 <- " 
 #path c (direct effect) 
 lte1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_lte1 <- " 
 #path c (direct effect) 
 lte1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 ltm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_lts1 <- " 
 #path c (direct effect) 
 lts1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 lts1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lts1 <- " 
 #path c (direct effect) 
 lts1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_lts1 <- " 
 #path c (direct effect) 
 lts1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_lts1 <- " 
 #path c (direct effect) 
 lts1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lts1 <- " 
 #path c (direct effect) 
 lts1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_lts1 <- " 
 #path c (direct effect) 
 lts1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 lt3a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 lt2a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_pollution_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*Cog_corrected_FRC_1 
 # path a
 pollution~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*Cog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*Cog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "

1.6 Specify Noncognitive_previous 138 models

NonCog_corrected_FRC_1_pollution_gte1 <- " 
 #path c (direct effect) 
 gte1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 gte1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_gte1 <- " 
 #path c (direct effect) 
 gte1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_gte1 <- " 
 #path c (direct effect) 
 gte1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_gte1 <- " 
 #path c (direct effect) 
 gte1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_gte1 <- " 
 #path c (direct effect) 
 gte1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_gte1 <- " 
 #path c (direct effect) 
 gte1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 gte1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 gtm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_gtm1 <- " 
 #path c (direct effect) 
 gtm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 gtm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_gta1 <- " 
 #path c (direct effect) 
 gta1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 gta1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_gta1 <- " 
 #path c (direct effect) 
 gta1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_gta1 <- " 
 #path c (direct effect) 
 gta1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_gta1 <- " 
 #path c (direct effect) 
 gta1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_gta1 <- " 
 #path c (direct effect) 
 gta1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_gta1 <- " 
 #path c (direct effect) 
 gta1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 gta1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_ite1 <- " 
 #path c (direct effect) 
 ite1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 ite1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_ite1 <- " 
 #path c (direct effect) 
 ite1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_ite1 <- " 
 #path c (direct effect) 
 ite1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_ite1 <- " 
 #path c (direct effect) 
 ite1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_ite1 <- " 
 #path c (direct effect) 
 ite1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_ite1 <- " 
 #path c (direct effect) 
 ite1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 ite1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_itm1 <- " 
 #path c (direct effect) 
 itm1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 itm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_itm1 <- " 
 #path c (direct effect) 
 itm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_itm1 <- " 
 #path c (direct effect) 
 itm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_itm1 <- " 
 #path c (direct effect) 
 itm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_itm1 <- " 
 #path c (direct effect) 
 itm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_itm1 <- " 
 #path c (direct effect) 
 itm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 itm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_its1 <- " 
 #path c (direct effect) 
 its1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 its1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_its1 <- " 
 #path c (direct effect) 
 its1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_its1 <- " 
 #path c (direct effect) 
 its1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_its1 <- " 
 #path c (direct effect) 
 its1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_its1 <- " 
 #path c (direct effect) 
 its1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_its1 <- " 
 #path c (direct effect) 
 its1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 its1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 it3a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_it3a1 <- " 
 #path c (direct effect) 
 it3a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 it3a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 it2a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_it2a1 <- " 
 #path c (direct effect) 
 it2a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 it2a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_lte1 <- " 
 #path c (direct effect) 
 lte1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 lte1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lte1 <- " 
 #path c (direct effect) 
 lte1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_lte1 <- " 
 #path c (direct effect) 
 lte1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_lte1 <- " 
 #path c (direct effect) 
 lte1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lte1 <- " 
 #path c (direct effect) 
 lte1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_lte1 <- " 
 #path c (direct effect) 
 lte1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 lte1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 ltm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_ltm1 <- " 
 #path c (direct effect) 
 ltm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 ltm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_lts1 <- " 
 #path c (direct effect) 
 lts1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 lts1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lts1 <- " 
 #path c (direct effect) 
 lts1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_lts1 <- " 
 #path c (direct effect) 
 lts1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_lts1 <- " 
 #path c (direct effect) 
 lts1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lts1 <- " 
 #path c (direct effect) 
 lts1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_lts1 <- " 
 #path c (direct effect) 
 lts1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 lts1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 lt3a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_lt3a1 <- " 
 #path c (direct effect) 
 lt3a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 lt3a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 lt2a1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_lt2a1 <- " 
 #path c (direct effect) 
 lt2a1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 lt2a1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1 <- " 
 #path c (direct effect) 
 pcexgcsenum1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsenum1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1 <- " 
 #path c (direct effect) 
 pcexgcseabcnum1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcseabcnum1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1 <- " 
 #path c (direct effect) 
 pcexgcsepsct1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsepsct1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1 <- " 
 #path c (direct effect) 
 pcexgcsegrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsegrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1 <- " 
 #path c (direct effect) 
 pcexothergrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 pcexothergrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1 <- " 
 #path c (direct effect) 
 pcexadvgrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 pcexadvgrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1 <- " 
 #path c (direct effect) 
 pcexallgrdm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 pcexallgrdm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1 <- " 
 #path c (direct effect) 
 pcexgcseengm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcseengm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1 <- " 
 #path c (direct effect) 
 pcexgcsescim1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsescim1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1 <- " 
 #path c (direct effect) 
 pcexgcsematm1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsematm1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_pollution_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*NonCog_corrected_FRC_1 
 # path a
 pollution~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*pollution 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_occupancyrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_occupancyrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_healthrating~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_healthrating 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_householdsize~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_householdsize 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_quality_populinhouseholds~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_quality_populinhouseholds 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "
NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1 <- " 
 #path c (direct effect) 
 pcexgcsecorem1~c*NonCog_corrected_FRC_1 
 # path a
 neighbourhood_economy_qualification~a*NonCog_corrected_FRC_1 
 # path b
 pcexgcsecorem1~b*neighbourhood_economy_qualification 
 
 indirect := a*b 
 direct := c 
 total := c + (a*b) 
 "

2 Fit all models, remove outliers for each model before runing

2.1 EA models

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsenum1 M: pollution 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1<- dat[dat$pollution < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1<- dat[dat$pollution > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~pollution+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcsenum1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsenum1 M: neighbourhood_quality_occupancyrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_occupancyrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsenum1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsenum1 M: neighbourhood_quality_healthrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_healthrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsenum1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsenum1 M: neighbourhood_quality_householdsize 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_householdsize+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsenum1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsenum1 M: neighbourhood_quality_populinhouseholds 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_populinhouseholds+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsenum1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsenum1 M: neighbourhood_economy_qualification 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_economy_qualification+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsenum1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcseabcnum1 M: pollution 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1<- dat[dat$pollution < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1<- dat[dat$pollution > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~pollution+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcseabcnum1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcseabcnum1 M: neighbourhood_quality_occupancyrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_occupancyrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcseabcnum1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcseabcnum1 M: neighbourhood_quality_healthrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_healthrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcseabcnum1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcseabcnum1 M: neighbourhood_quality_householdsize 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_householdsize+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcseabcnum1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcseabcnum1 M: neighbourhood_quality_populinhouseholds 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_populinhouseholds+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcseabcnum1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcseabcnum1 M: neighbourhood_economy_qualification 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_economy_qualification+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcseabcnum1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsepsct1 M: pollution 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1<- dat[dat$pollution < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1<- dat[dat$pollution > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~pollution+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcsepsct1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsepsct1 M: neighbourhood_quality_occupancyrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_occupancyrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsepsct1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsepsct1 M: neighbourhood_quality_healthrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_healthrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsepsct1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsepsct1 M: neighbourhood_quality_householdsize 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_householdsize+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsepsct1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsepsct1 M: neighbourhood_quality_populinhouseholds 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_populinhouseholds+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsepsct1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsepsct1 M: neighbourhood_economy_qualification 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_economy_qualification+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsepsct1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsegrdm1 M: pollution 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1<- dat[dat$pollution < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1<- dat[dat$pollution > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~pollution+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcsegrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsegrdm1 M: neighbourhood_quality_occupancyrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_occupancyrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsegrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsegrdm1 M: neighbourhood_quality_healthrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_healthrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsegrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsegrdm1 M: neighbourhood_quality_householdsize 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_householdsize+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsegrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsegrdm1 M: neighbourhood_quality_populinhouseholds 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_populinhouseholds+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsegrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsegrdm1 M: neighbourhood_economy_qualification 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_economy_qualification+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsegrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexothergrdm1 M: pollution 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1<- dat[dat$pollution < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1<- dat[dat$pollution > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~pollution+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="pollution",EduAch="pcexothergrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexothergrdm1 M: neighbourhood_quality_occupancyrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_occupancyrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexothergrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexothergrdm1 M: neighbourhood_quality_healthrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_healthrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexothergrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexothergrdm1 M: neighbourhood_quality_householdsize 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_householdsize+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexothergrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexothergrdm1 M: neighbourhood_quality_populinhouseholds 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_populinhouseholds+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexothergrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexothergrdm1 M: neighbourhood_economy_qualification 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_economy_qualification+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexothergrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexadvgrdm1 M: pollution 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1<- dat[dat$pollution < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1<- dat[dat$pollution > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~pollution+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="pollution",EduAch="pcexadvgrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexadvgrdm1 M: neighbourhood_quality_occupancyrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_occupancyrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexadvgrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexadvgrdm1 M: neighbourhood_quality_healthrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_healthrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexadvgrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexadvgrdm1 M: neighbourhood_quality_householdsize 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_householdsize+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexadvgrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexadvgrdm1 M: neighbourhood_quality_populinhouseholds 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_populinhouseholds+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexadvgrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexadvgrdm1 M: neighbourhood_economy_qualification 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_economy_qualification+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexadvgrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexallgrdm1 M: pollution 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1<- dat[dat$pollution < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1<- dat[dat$pollution > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~pollution+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="pollution",EduAch="pcexallgrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexallgrdm1 M: neighbourhood_quality_occupancyrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_occupancyrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexallgrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexallgrdm1 M: neighbourhood_quality_healthrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_healthrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexallgrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexallgrdm1 M: neighbourhood_quality_householdsize 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_householdsize+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexallgrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexallgrdm1 M: neighbourhood_quality_populinhouseholds 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_populinhouseholds+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexallgrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexallgrdm1 M: neighbourhood_economy_qualification 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_economy_qualification+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexallgrdm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcseengm1 M: pollution 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1<- dat[dat$pollution < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1<- dat[dat$pollution > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~pollution+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcseengm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcseengm1 M: neighbourhood_quality_occupancyrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_occupancyrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcseengm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcseengm1 M: neighbourhood_quality_healthrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_healthrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcseengm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcseengm1 M: neighbourhood_quality_householdsize 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_householdsize+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcseengm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcseengm1 M: neighbourhood_quality_populinhouseholds 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_populinhouseholds+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcseengm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcseengm1 M: neighbourhood_economy_qualification 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_economy_qualification+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcseengm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsescim1 M: pollution 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1<- dat[dat$pollution < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1<- dat[dat$pollution > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~pollution+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcsescim1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsescim1 M: neighbourhood_quality_occupancyrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_occupancyrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsescim1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsescim1 M: neighbourhood_quality_healthrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_healthrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsescim1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsescim1 M: neighbourhood_quality_householdsize 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_householdsize+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsescim1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsescim1 M: neighbourhood_quality_populinhouseholds 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_populinhouseholds+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsescim1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsescim1 M: neighbourhood_economy_qualification 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_economy_qualification+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsescim1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsematm1 M: pollution 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1<- dat[dat$pollution < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1<- dat[dat$pollution > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~pollution+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcsematm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsematm1 M: neighbourhood_quality_occupancyrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_occupancyrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsematm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsematm1 M: neighbourhood_quality_healthrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_healthrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsematm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsematm1 M: neighbourhood_quality_householdsize 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_householdsize+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsematm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsematm1 M: neighbourhood_quality_populinhouseholds 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_populinhouseholds+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsematm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsematm1 M: neighbourhood_economy_qualification 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_economy_qualification+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsematm1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsecorem1 M: pollution 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1<- dat[dat$pollution < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1<- dat[dat$pollution > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~pollution+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcsecorem1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsecorem1 M: neighbourhood_quality_occupancyrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_occupancyrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsecorem1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsecorem1 M: neighbourhood_quality_healthrating 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_healthrating+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsecorem1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsecorem1 M: neighbourhood_quality_householdsize 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_householdsize+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsecorem1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsecorem1 M: neighbourhood_quality_populinhouseholds 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_populinhouseholds+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsecorem1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,Model_fit)

### GPS: EA3_Lee2018_no23andme_corrected_FRCT1 EA: pcexgcsecorem1 M: neighbourhood_economy_qualification 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- sem(EA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1,dat = datEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1) 
#summary(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- parameterEstimates(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1, output = "data.frame") 
#output_fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_economy_qualification+EA3_Lee2018_no23andme_corrected_FRCT1,dat=dat) 
#semPaths(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1") 
Model_est <- broom::tidy(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="EA3_Lee2018_no23andme_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsecorem1",Model_est) 
ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1) 
ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- cbind(ParamEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1,Model_fit)

2.2 IQ models

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsenum1 M: pollution 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1<- dat[dat$pollution < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1<- dat[dat$pollution > -4,] 

fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1<- sem(IQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1,dat = datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~pollution+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcsenum1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1) 
ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsenum1 M: neighbourhood_quality_occupancyrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_occupancyrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsenum1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsenum1 M: neighbourhood_quality_healthrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_healthrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsenum1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsenum1 M: neighbourhood_quality_householdsize 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_householdsize+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsenum1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsenum1 M: neighbourhood_quality_populinhouseholds 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_populinhouseholds+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsenum1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsenum1 M: neighbourhood_economy_qualification 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_economy_qualification+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsenum1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcseabcnum1 M: pollution 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1<- dat[dat$pollution < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1<- dat[dat$pollution > -4,] 

fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1<- sem(IQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1,dat = datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~pollution+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcseabcnum1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1) 
ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcseabcnum1 M: neighbourhood_quality_occupancyrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_occupancyrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcseabcnum1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcseabcnum1 M: neighbourhood_quality_healthrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_healthrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcseabcnum1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcseabcnum1 M: neighbourhood_quality_householdsize 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_householdsize+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcseabcnum1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcseabcnum1 M: neighbourhood_quality_populinhouseholds 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_populinhouseholds+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcseabcnum1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcseabcnum1 M: neighbourhood_economy_qualification 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_economy_qualification+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcseabcnum1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsepsct1 M: pollution 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1<- dat[dat$pollution < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1<- dat[dat$pollution > -4,] 

fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1<- sem(IQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1,dat = datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~pollution+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcsepsct1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1) 
ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsepsct1 M: neighbourhood_quality_occupancyrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_occupancyrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsepsct1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsepsct1 M: neighbourhood_quality_healthrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_healthrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsepsct1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsepsct1 M: neighbourhood_quality_householdsize 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_householdsize+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsepsct1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsepsct1 M: neighbourhood_quality_populinhouseholds 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_populinhouseholds+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsepsct1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsepsct1 M: neighbourhood_economy_qualification 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_economy_qualification+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsepsct1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsegrdm1 M: pollution 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1<- dat[dat$pollution < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1<- dat[dat$pollution > -4,] 

fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1<- sem(IQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1,dat = datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~pollution+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcsegrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsegrdm1 M: neighbourhood_quality_occupancyrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_occupancyrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsegrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsegrdm1 M: neighbourhood_quality_healthrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_healthrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsegrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsegrdm1 M: neighbourhood_quality_householdsize 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_householdsize+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsegrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsegrdm1 M: neighbourhood_quality_populinhouseholds 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_populinhouseholds+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsegrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsegrdm1 M: neighbourhood_economy_qualification 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_economy_qualification+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsegrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexothergrdm1 M: pollution 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1<- dat[dat$pollution < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1<- dat[dat$pollution > -4,] 

fitIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1<- sem(IQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1,dat = datIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~pollution+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="pollution",EduAch="pcexothergrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexothergrdm1 M: neighbourhood_quality_occupancyrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_occupancyrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexothergrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexothergrdm1 M: neighbourhood_quality_healthrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_healthrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexothergrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexothergrdm1 M: neighbourhood_quality_householdsize 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_householdsize+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexothergrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexothergrdm1 M: neighbourhood_quality_populinhouseholds 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_populinhouseholds+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexothergrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexothergrdm1 M: neighbourhood_economy_qualification 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_economy_qualification+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexothergrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexadvgrdm1 M: pollution 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1<- dat[dat$pollution < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1<- dat[dat$pollution > -4,] 

fitIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1<- sem(IQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1,dat = datIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~pollution+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="pollution",EduAch="pcexadvgrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexadvgrdm1 M: neighbourhood_quality_occupancyrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_occupancyrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexadvgrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexadvgrdm1 M: neighbourhood_quality_healthrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_healthrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexadvgrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexadvgrdm1 M: neighbourhood_quality_householdsize 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_householdsize+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexadvgrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexadvgrdm1 M: neighbourhood_quality_populinhouseholds 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_populinhouseholds+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexadvgrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexadvgrdm1 M: neighbourhood_economy_qualification 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_economy_qualification+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexadvgrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexallgrdm1 M: pollution 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1<- dat[dat$pollution < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1<- dat[dat$pollution > -4,] 

fitIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1<- sem(IQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1,dat = datIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~pollution+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="pollution",EduAch="pcexallgrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexallgrdm1 M: neighbourhood_quality_occupancyrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_occupancyrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexallgrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexallgrdm1 M: neighbourhood_quality_healthrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_healthrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexallgrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexallgrdm1 M: neighbourhood_quality_householdsize 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_householdsize+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexallgrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexallgrdm1 M: neighbourhood_quality_populinhouseholds 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_populinhouseholds+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexallgrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexallgrdm1 M: neighbourhood_economy_qualification 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_economy_qualification+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexallgrdm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcseengm1 M: pollution 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1<- dat[dat$pollution < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1<- dat[dat$pollution > -4,] 

fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1<- sem(IQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1,dat = datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~pollution+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcseengm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcseengm1 M: neighbourhood_quality_occupancyrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_occupancyrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcseengm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcseengm1 M: neighbourhood_quality_healthrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_healthrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcseengm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcseengm1 M: neighbourhood_quality_householdsize 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_householdsize+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcseengm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcseengm1 M: neighbourhood_quality_populinhouseholds 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_populinhouseholds+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcseengm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcseengm1 M: neighbourhood_economy_qualification 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_economy_qualification+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcseengm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsescim1 M: pollution 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1<- dat[dat$pollution < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1<- dat[dat$pollution > -4,] 

fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1<- sem(IQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1,dat = datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~pollution+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcsescim1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1) 
ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsescim1 M: neighbourhood_quality_occupancyrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_occupancyrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsescim1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsescim1 M: neighbourhood_quality_healthrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_healthrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsescim1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsescim1 M: neighbourhood_quality_householdsize 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_householdsize+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsescim1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsescim1 M: neighbourhood_quality_populinhouseholds 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_populinhouseholds+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsescim1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsescim1 M: neighbourhood_economy_qualification 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_economy_qualification+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsescim1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsematm1 M: pollution 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1<- dat[dat$pollution < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1<- dat[dat$pollution > -4,] 

fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1<- sem(IQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1,dat = datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~pollution+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcsematm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsematm1 M: neighbourhood_quality_occupancyrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_occupancyrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsematm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsematm1 M: neighbourhood_quality_healthrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_healthrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsematm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsematm1 M: neighbourhood_quality_householdsize 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_householdsize+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsematm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsematm1 M: neighbourhood_quality_populinhouseholds 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_populinhouseholds+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsematm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsematm1 M: neighbourhood_economy_qualification 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_economy_qualification+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsematm1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsecorem1 M: pollution 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1<- dat[dat$pollution < 4,] 
datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1<- dat[dat$pollution > -4,] 

fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1<- sem(IQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1,dat = datIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~pollution+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="pollution",EduAch="pcexgcsecorem1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1) 
ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsecorem1 M: neighbourhood_quality_occupancyrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_occupancyrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsecorem1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsecorem1 M: neighbourhood_quality_healthrating 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_healthrating+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsecorem1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsecorem1 M: neighbourhood_quality_householdsize 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_householdsize+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsecorem1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsecorem1 M: neighbourhood_quality_populinhouseholds 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_populinhouseholds+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsecorem1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,Model_fit)

### GPS: IQ_Savage2018_corrected_FRCT1 EA: pcexgcsecorem1 M: neighbourhood_economy_qualification 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- sem(IQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1,dat = datIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1) 
#summary(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- parameterEstimates(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1, output = "data.frame") 
#output_fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_economy_qualification+IQ_Savage2018_corrected_FRCT1,dat=dat) 
#semPaths(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1") 
Model_est <- broom::tidy(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="IQ_Savage2018_corrected_FRCT1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsecorem1",Model_est) 
ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1) 
ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1<- cbind(ParamIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1,Model_fit)

2.3 Cognitive_extended models

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsenum1 M: pollution 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pollution < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pollution > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- sem(c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1,dat = datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~pollution+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsenum1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_occupancyrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_occupancyrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsenum1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_healthrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_healthrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsenum1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_householdsize 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_householdsize+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsenum1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_populinhouseholds 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_populinhouseholds+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsenum1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_economy_qualification 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_economy_qualification+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsenum1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseabcnum1 M: pollution 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pollution < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pollution > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- sem(c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1,dat = datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~pollution+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcseabcnum1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_occupancyrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_occupancyrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcseabcnum1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_healthrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_healthrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcseabcnum1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_householdsize 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_householdsize+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcseabcnum1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_populinhouseholds 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_populinhouseholds+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcseabcnum1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_economy_qualification 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_economy_qualification+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcseabcnum1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsepsct1 M: pollution 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pollution < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pollution > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- sem(c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1,dat = datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~pollution+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsepsct1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_occupancyrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_occupancyrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsepsct1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_healthrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_healthrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsepsct1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_householdsize 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_householdsize+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsepsct1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_populinhouseholds 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_populinhouseholds+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsepsct1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_economy_qualification 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_economy_qualification+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsepsct1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsegrdm1 M: pollution 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pollution < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pollution > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~pollution+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsegrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_occupancyrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_occupancyrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsegrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_healthrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_healthrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsegrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_householdsize 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_householdsize+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsegrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_populinhouseholds 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_populinhouseholds+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsegrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_economy_qualification 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_economy_qualification+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsegrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexothergrdm1 M: pollution 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pollution < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pollution > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~pollution+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexothergrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_occupancyrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_occupancyrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexothergrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_healthrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_healthrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexothergrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_householdsize 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_householdsize+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexothergrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_populinhouseholds 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_populinhouseholds+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexothergrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_economy_qualification 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_economy_qualification+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexothergrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexadvgrdm1 M: pollution 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pollution < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pollution > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~pollution+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexadvgrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_occupancyrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_occupancyrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexadvgrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_healthrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_healthrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexadvgrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_householdsize 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_householdsize+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexadvgrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_populinhouseholds 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_populinhouseholds+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexadvgrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_economy_qualification 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_economy_qualification+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexadvgrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexallgrdm1 M: pollution 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pollution < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pollution > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~pollution+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexallgrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_occupancyrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_occupancyrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexallgrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_healthrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_healthrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexallgrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_householdsize 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_householdsize+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexallgrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_populinhouseholds 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_populinhouseholds+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexallgrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_economy_qualification 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_economy_qualification+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexallgrdm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseengm1 M: pollution 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pollution < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pollution > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- sem(c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1,dat = datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~pollution+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcseengm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_occupancyrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_occupancyrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcseengm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_healthrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_healthrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcseengm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_householdsize 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_householdsize+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcseengm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_populinhouseholds 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_populinhouseholds+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcseengm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_economy_qualification 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_economy_qualification+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcseengm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsescim1 M: pollution 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pollution < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pollution > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- sem(c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1,dat = datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~pollution+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsescim1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_occupancyrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_occupancyrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsescim1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_healthrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_healthrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsescim1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_householdsize 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_householdsize+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsescim1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_populinhouseholds 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_populinhouseholds+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsescim1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_economy_qualification 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_economy_qualification+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsescim1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsematm1 M: pollution 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pollution < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pollution > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- sem(c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1,dat = datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~pollution+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsematm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_occupancyrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_occupancyrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsematm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_healthrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_healthrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsematm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_householdsize 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_householdsize+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsematm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_populinhouseholds 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_populinhouseholds+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsematm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_economy_qualification 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_economy_qualification+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsematm1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsecorem1 M: pollution 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pollution < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pollution > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- sem(c_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1,dat = datc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~pollution+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsecorem1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_occupancyrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_occupancyrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsecorem1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_healthrating 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_healthrating+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsecorem1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_householdsize 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_householdsize+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsecorem1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_populinhouseholds 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_populinhouseholds+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsecorem1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,Model_fit)

### GPS: c_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_economy_qualification 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- sem(c_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,dat = datc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1) 
#summary(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- parameterEstimates(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1, output = "data.frame") 
#output_fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_economy_qualification+c_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1") 
Model_est <- broom::tidy(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="c_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsecorem1",Model_est) 
Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1) 
Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- cbind(Paramc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,Model_fit)

2.4 NonCognitive_extended models

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsenum1 M: pollution 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pollution < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pollution > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- sem(nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1,dat = datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~pollution+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsenum1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_occupancyrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_occupancyrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsenum1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_healthrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_healthrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsenum1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_householdsize 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_householdsize+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsenum1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_populinhouseholds 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_populinhouseholds+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsenum1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_economy_qualification 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_economy_qualification+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsenum1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseabcnum1 M: pollution 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pollution < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pollution > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- sem(nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1,dat = datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~pollution+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcseabcnum1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_occupancyrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_occupancyrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcseabcnum1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_healthrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_healthrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcseabcnum1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_householdsize 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_householdsize+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcseabcnum1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_populinhouseholds 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_populinhouseholds+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcseabcnum1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_economy_qualification 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_economy_qualification+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcseabcnum1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsepsct1 M: pollution 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pollution < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pollution > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- sem(nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1,dat = datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~pollution+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsepsct1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_occupancyrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_occupancyrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsepsct1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_healthrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_healthrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsepsct1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_householdsize 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_householdsize+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsepsct1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_populinhouseholds 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_populinhouseholds+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsepsct1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_economy_qualification 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_economy_qualification+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsepsct1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsegrdm1 M: pollution 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pollution < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pollution > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~pollution+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsegrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_occupancyrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_occupancyrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsegrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_healthrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_healthrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsegrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_householdsize 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_householdsize+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsegrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_populinhouseholds 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_populinhouseholds+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsegrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_economy_qualification 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_economy_qualification+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsegrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexothergrdm1 M: pollution 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pollution < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pollution > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~pollution+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexothergrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_occupancyrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_occupancyrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexothergrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_healthrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_healthrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexothergrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_householdsize 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_householdsize+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexothergrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_populinhouseholds 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_populinhouseholds+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexothergrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_economy_qualification 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_economy_qualification+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexothergrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexadvgrdm1 M: pollution 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pollution < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pollution > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~pollution+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexadvgrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_occupancyrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_occupancyrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexadvgrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_healthrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_healthrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexadvgrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_householdsize 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_householdsize+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexadvgrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_populinhouseholds 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_populinhouseholds+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexadvgrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_economy_qualification 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_economy_qualification+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexadvgrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexallgrdm1 M: pollution 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pollution < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pollution > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~pollution+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexallgrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_occupancyrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_occupancyrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexallgrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_healthrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_healthrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexallgrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_householdsize 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_householdsize+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexallgrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_populinhouseholds 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_populinhouseholds+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexallgrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_economy_qualification 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_economy_qualification+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexallgrdm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseengm1 M: pollution 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pollution < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pollution > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~pollution+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcseengm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_occupancyrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_occupancyrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcseengm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_healthrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_healthrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcseengm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_householdsize 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_householdsize+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcseengm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_populinhouseholds 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_populinhouseholds+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcseengm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_economy_qualification 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_economy_qualification+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcseengm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsescim1 M: pollution 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pollution < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pollution > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- sem(nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1,dat = datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~pollution+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsescim1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_occupancyrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_occupancyrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsescim1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_healthrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_healthrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsescim1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_householdsize 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_householdsize+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsescim1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_populinhouseholds 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_populinhouseholds+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsescim1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_economy_qualification 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_economy_qualification+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsescim1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsematm1 M: pollution 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pollution < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pollution > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~pollution+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsematm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_occupancyrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_occupancyrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsematm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_healthrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_healthrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsematm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_householdsize 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_householdsize+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsematm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_populinhouseholds 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_populinhouseholds+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsematm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_economy_qualification 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_economy_qualification+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsematm1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsecorem1 M: pollution 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pollution < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pollution > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- sem(nc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1,dat = datnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~pollution+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsecorem1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_occupancyrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_occupancyrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsecorem1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_healthrating 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_healthrating+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsecorem1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_householdsize 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_householdsize+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsecorem1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_populinhouseholds 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_populinhouseholds+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsecorem1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,Model_fit)

### GPS: nc_chol_cog_noncog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_economy_qualification 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- sem(nc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,dat = datnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1) 
#summary(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- parameterEstimates(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1, output = "data.frame") 
#output_fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_economy_qualification+nc_chol_cog_noncog_corrected_FRC_1,dat=dat) 
#semPaths(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1") 
Model_est <- broom::tidy(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="nc_chol_cog_noncog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsecorem1",Model_est) 
Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1) 
Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- cbind(Paramnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,Model_fit)

2.5 Cognitive_previous models

### GPS: Cog_corrected_FRC_1 EA: pcexgcsenum1 M: pollution 
datCog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datCog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pollution < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pollution > -4,] 

fitCog_corrected_FRC_1_pollution_pcexgcsenum1<- sem(Cog_corrected_FRC_1_pollution_pcexgcsenum1,dat = datCog_corrected_FRC_1_pollution_pcexgcsenum1) 
#summary(fitCog_corrected_FRC_1_pollution_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_pollution_pcexgcsenum1<- parameterEstimates(fitCog_corrected_FRC_1_pollution_pcexgcsenum1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_pollution_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~pollution+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_pollution_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_pollution_pcexgcsenum1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_pollution_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsenum1",Model_est) 
ParamCog_corrected_FRC_1_pollution_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_pollution_pcexgcsenum1) 
ParamfitCog_corrected_FRC_1_pollution_pcexgcsenum1<- cbind(ParamCog_corrected_FRC_1_pollution_pcexgcsenum1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_occupancyrating 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,dat = datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_occupancyrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsenum1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_healthrating 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,dat = datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_healthrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsenum1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_householdsize 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,dat = datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_householdsize+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsenum1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_populinhouseholds 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,dat = datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_populinhouseholds+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsenum1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_economy_qualification 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- sem(Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,dat = datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_economy_qualification+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsenum1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1) 
ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcseabcnum1 M: pollution 
datCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pollution < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pollution > -4,] 

fitCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- sem(Cog_corrected_FRC_1_pollution_pcexgcseabcnum1,dat = datCog_corrected_FRC_1_pollution_pcexgcseabcnum1) 
#summary(fitCog_corrected_FRC_1_pollution_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- parameterEstimates(fitCog_corrected_FRC_1_pollution_pcexgcseabcnum1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_pollution_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~pollution+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_pollution_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_pollution_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_pollution_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcseabcnum1",Model_est) 
ParamCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_pollution_pcexgcseabcnum1) 
ParamfitCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- cbind(ParamCog_corrected_FRC_1_pollution_pcexgcseabcnum1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_occupancyrating 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,dat = datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_occupancyrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcseabcnum1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_healthrating 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,dat = datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_healthrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcseabcnum1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_householdsize 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,dat = datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_householdsize+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcseabcnum1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_populinhouseholds 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,dat = datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_populinhouseholds+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcseabcnum1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_economy_qualification 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- sem(Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,dat = datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_economy_qualification+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcseabcnum1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1) 
ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsepsct1 M: pollution 
datCog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datCog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pollution < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pollution > -4,] 

fitCog_corrected_FRC_1_pollution_pcexgcsepsct1<- sem(Cog_corrected_FRC_1_pollution_pcexgcsepsct1,dat = datCog_corrected_FRC_1_pollution_pcexgcsepsct1) 
#summary(fitCog_corrected_FRC_1_pollution_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_pollution_pcexgcsepsct1<- parameterEstimates(fitCog_corrected_FRC_1_pollution_pcexgcsepsct1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_pollution_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~pollution+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_pollution_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_pollution_pcexgcsepsct1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_pollution_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsepsct1",Model_est) 
ParamCog_corrected_FRC_1_pollution_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_pollution_pcexgcsepsct1) 
ParamfitCog_corrected_FRC_1_pollution_pcexgcsepsct1<- cbind(ParamCog_corrected_FRC_1_pollution_pcexgcsepsct1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_occupancyrating 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,dat = datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_occupancyrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsepsct1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_healthrating 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,dat = datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_healthrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsepsct1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_householdsize 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,dat = datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_householdsize+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsepsct1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_populinhouseholds 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,dat = datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_populinhouseholds+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsepsct1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_economy_qualification 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- sem(Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,dat = datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_economy_qualification+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsepsct1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1) 
ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsegrdm1 M: pollution 
datCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pollution < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pollution > -4,] 

fitCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- sem(Cog_corrected_FRC_1_pollution_pcexgcsegrdm1,dat = datCog_corrected_FRC_1_pollution_pcexgcsegrdm1) 
#summary(fitCog_corrected_FRC_1_pollution_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- parameterEstimates(fitCog_corrected_FRC_1_pollution_pcexgcsegrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_pollution_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~pollution+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_pollution_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_pollution_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_pollution_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsegrdm1",Model_est) 
ParamCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_pollution_pcexgcsegrdm1) 
ParamfitCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- cbind(ParamCog_corrected_FRC_1_pollution_pcexgcsegrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_occupancyrating 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_occupancyrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsegrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_healthrating 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_healthrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsegrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_householdsize 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_householdsize+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsegrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_populinhouseholds 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_populinhouseholds+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsegrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_economy_qualification 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,dat = datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_economy_qualification+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsegrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexothergrdm1 M: pollution 
datCog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datCog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datCog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pollution < 4,] 
datCog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pollution > -4,] 

fitCog_corrected_FRC_1_pollution_pcexothergrdm1<- sem(Cog_corrected_FRC_1_pollution_pcexothergrdm1,dat = datCog_corrected_FRC_1_pollution_pcexothergrdm1) 
#summary(fitCog_corrected_FRC_1_pollution_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_pollution_pcexothergrdm1<- parameterEstimates(fitCog_corrected_FRC_1_pollution_pcexothergrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_pollution_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~pollution+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_pollution_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_pollution_pcexothergrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_pollution_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="pollution",EduAch="pcexothergrdm1",Model_est) 
ParamCog_corrected_FRC_1_pollution_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_pollution_pcexothergrdm1) 
ParamfitCog_corrected_FRC_1_pollution_pcexothergrdm1<- cbind(ParamCog_corrected_FRC_1_pollution_pcexothergrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_occupancyrating 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_occupancyrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexothergrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_healthrating 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_healthrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexothergrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_householdsize 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_householdsize+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexothergrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_populinhouseholds 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_populinhouseholds+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexothergrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_economy_qualification 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,dat = datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_economy_qualification+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexothergrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexadvgrdm1 M: pollution 
datCog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datCog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datCog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pollution < 4,] 
datCog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pollution > -4,] 

fitCog_corrected_FRC_1_pollution_pcexadvgrdm1<- sem(Cog_corrected_FRC_1_pollution_pcexadvgrdm1,dat = datCog_corrected_FRC_1_pollution_pcexadvgrdm1) 
#summary(fitCog_corrected_FRC_1_pollution_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_pollution_pcexadvgrdm1<- parameterEstimates(fitCog_corrected_FRC_1_pollution_pcexadvgrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_pollution_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~pollution+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_pollution_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_pollution_pcexadvgrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_pollution_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="pollution",EduAch="pcexadvgrdm1",Model_est) 
ParamCog_corrected_FRC_1_pollution_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_pollution_pcexadvgrdm1) 
ParamfitCog_corrected_FRC_1_pollution_pcexadvgrdm1<- cbind(ParamCog_corrected_FRC_1_pollution_pcexadvgrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_occupancyrating 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_occupancyrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexadvgrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_healthrating 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_healthrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexadvgrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_householdsize 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_householdsize+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexadvgrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_populinhouseholds 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_populinhouseholds+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexadvgrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_economy_qualification 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,dat = datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_economy_qualification+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexadvgrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexallgrdm1 M: pollution 
datCog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datCog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datCog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pollution < 4,] 
datCog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pollution > -4,] 

fitCog_corrected_FRC_1_pollution_pcexallgrdm1<- sem(Cog_corrected_FRC_1_pollution_pcexallgrdm1,dat = datCog_corrected_FRC_1_pollution_pcexallgrdm1) 
#summary(fitCog_corrected_FRC_1_pollution_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_pollution_pcexallgrdm1<- parameterEstimates(fitCog_corrected_FRC_1_pollution_pcexallgrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_pollution_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~pollution+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_pollution_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_pollution_pcexallgrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_pollution_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="pollution",EduAch="pcexallgrdm1",Model_est) 
ParamCog_corrected_FRC_1_pollution_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_pollution_pcexallgrdm1) 
ParamfitCog_corrected_FRC_1_pollution_pcexallgrdm1<- cbind(ParamCog_corrected_FRC_1_pollution_pcexallgrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_occupancyrating 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_occupancyrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexallgrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_healthrating 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_healthrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexallgrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_householdsize 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_householdsize+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexallgrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_populinhouseholds 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_populinhouseholds+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexallgrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_economy_qualification 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- sem(Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,dat = datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_economy_qualification+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexallgrdm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcseengm1 M: pollution 
datCog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datCog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pollution < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pollution > -4,] 

fitCog_corrected_FRC_1_pollution_pcexgcseengm1<- sem(Cog_corrected_FRC_1_pollution_pcexgcseengm1,dat = datCog_corrected_FRC_1_pollution_pcexgcseengm1) 
#summary(fitCog_corrected_FRC_1_pollution_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_pollution_pcexgcseengm1<- parameterEstimates(fitCog_corrected_FRC_1_pollution_pcexgcseengm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_pollution_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~pollution+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_pollution_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_pollution_pcexgcseengm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_pollution_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcseengm1",Model_est) 
ParamCog_corrected_FRC_1_pollution_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_pollution_pcexgcseengm1) 
ParamfitCog_corrected_FRC_1_pollution_pcexgcseengm1<- cbind(ParamCog_corrected_FRC_1_pollution_pcexgcseengm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_occupancyrating 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_occupancyrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcseengm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_healthrating 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_healthrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcseengm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_householdsize 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_householdsize+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcseengm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_populinhouseholds 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_populinhouseholds+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcseengm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_economy_qualification 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- sem(Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,dat = datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_economy_qualification+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcseengm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsescim1 M: pollution 
datCog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datCog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pollution < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pollution > -4,] 

fitCog_corrected_FRC_1_pollution_pcexgcsescim1<- sem(Cog_corrected_FRC_1_pollution_pcexgcsescim1,dat = datCog_corrected_FRC_1_pollution_pcexgcsescim1) 
#summary(fitCog_corrected_FRC_1_pollution_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_pollution_pcexgcsescim1<- parameterEstimates(fitCog_corrected_FRC_1_pollution_pcexgcsescim1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_pollution_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~pollution+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_pollution_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_pollution_pcexgcsescim1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_pollution_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsescim1",Model_est) 
ParamCog_corrected_FRC_1_pollution_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_pollution_pcexgcsescim1) 
ParamfitCog_corrected_FRC_1_pollution_pcexgcsescim1<- cbind(ParamCog_corrected_FRC_1_pollution_pcexgcsescim1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_occupancyrating 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,dat = datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_occupancyrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsescim1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_healthrating 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,dat = datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_healthrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsescim1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_householdsize 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,dat = datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_householdsize+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsescim1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_populinhouseholds 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,dat = datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_populinhouseholds+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsescim1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_economy_qualification 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- sem(Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,dat = datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_economy_qualification+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsescim1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1) 
ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsematm1 M: pollution 
datCog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datCog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pollution < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pollution > -4,] 

fitCog_corrected_FRC_1_pollution_pcexgcsematm1<- sem(Cog_corrected_FRC_1_pollution_pcexgcsematm1,dat = datCog_corrected_FRC_1_pollution_pcexgcsematm1) 
#summary(fitCog_corrected_FRC_1_pollution_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_pollution_pcexgcsematm1<- parameterEstimates(fitCog_corrected_FRC_1_pollution_pcexgcsematm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_pollution_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~pollution+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_pollution_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_pollution_pcexgcsematm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_pollution_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsematm1",Model_est) 
ParamCog_corrected_FRC_1_pollution_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_pollution_pcexgcsematm1) 
ParamfitCog_corrected_FRC_1_pollution_pcexgcsematm1<- cbind(ParamCog_corrected_FRC_1_pollution_pcexgcsematm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_occupancyrating 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_occupancyrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsematm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_healthrating 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_healthrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsematm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_householdsize 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_householdsize+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsematm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_populinhouseholds 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,dat = datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_populinhouseholds+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsematm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_economy_qualification 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- sem(Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,dat = datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_economy_qualification+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsematm1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1) 
ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsecorem1 M: pollution 
datCog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datCog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pollution < 4,] 
datCog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pollution > -4,] 

fitCog_corrected_FRC_1_pollution_pcexgcsecorem1<- sem(Cog_corrected_FRC_1_pollution_pcexgcsecorem1,dat = datCog_corrected_FRC_1_pollution_pcexgcsecorem1) 
#summary(fitCog_corrected_FRC_1_pollution_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_pollution_pcexgcsecorem1<- parameterEstimates(fitCog_corrected_FRC_1_pollution_pcexgcsecorem1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_pollution_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~pollution+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_pollution_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_pollution_pcexgcsecorem1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_pollution_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsecorem1",Model_est) 
ParamCog_corrected_FRC_1_pollution_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_pollution_pcexgcsecorem1) 
ParamfitCog_corrected_FRC_1_pollution_pcexgcsecorem1<- cbind(ParamCog_corrected_FRC_1_pollution_pcexgcsecorem1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_occupancyrating 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,dat = datCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_occupancyrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsecorem1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_healthrating 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,dat = datCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_healthrating+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsecorem1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_householdsize 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,dat = datCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_householdsize+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsecorem1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_populinhouseholds 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- sem(Cog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,dat = datCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_populinhouseholds+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsecorem1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1) 
ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,Model_fit)

### GPS: Cog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_economy_qualification 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- sem(Cog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,dat = datCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1) 
#summary(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- parameterEstimates(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1, output = "data.frame") 
#output_fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_economy_qualification+Cog_corrected_FRC_1,dat=dat) 
#semPaths(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1") 
Model_est <- broom::tidy(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="Cog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsecorem1",Model_est) 
ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1) 
ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- cbind(ParamCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,Model_fit)

2.6 NonCognitive_previous models

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsenum1 M: pollution 
datNonCog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pollution < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsenum1<- dat[dat$pollution > -4,] 

fitNonCog_corrected_FRC_1_pollution_pcexgcsenum1<- sem(NonCog_corrected_FRC_1_pollution_pcexgcsenum1,dat = datNonCog_corrected_FRC_1_pollution_pcexgcsenum1) 
#summary(fitNonCog_corrected_FRC_1_pollution_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcsenum1<- parameterEstimates(fitNonCog_corrected_FRC_1_pollution_pcexgcsenum1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~pollution+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_pollution_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_pollution_pcexgcsenum1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_pollution_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsenum1",Model_est) 
ParamNonCog_corrected_FRC_1_pollution_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_pollution_pcexgcsenum1) 
ParamfitNonCog_corrected_FRC_1_pollution_pcexgcsenum1<- cbind(ParamNonCog_corrected_FRC_1_pollution_pcexgcsenum1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_occupancyrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_occupancyrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsenum1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_healthrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_healthrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsenum1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_householdsize 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_householdsize+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsenum1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_quality_populinhouseholds 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_quality_populinhouseholds+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsenum1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsenum1 M: neighbourhood_economy_qualification 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$pcexgcsenum1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$pcexgcsenum1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- sem(NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,dat = datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1 
#mediate_plot(pcexgcsenum1~neighbourhood_economy_qualification+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsenum1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcseabcnum1 M: pollution 
datNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pollution < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- dat[dat$pollution > -4,] 

fitNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- sem(NonCog_corrected_FRC_1_pollution_pcexgcseabcnum1,dat = datNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1) 
#summary(fitNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- parameterEstimates(fitNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~pollution+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcseabcnum1",Model_est) 
ParamNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1) 
ParamfitNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1<- cbind(ParamNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_occupancyrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_occupancyrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcseabcnum1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_healthrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_healthrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcseabcnum1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_householdsize 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_householdsize+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcseabcnum1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_quality_populinhouseholds 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_quality_populinhouseholds+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcseabcnum1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcseabcnum1 M: neighbourhood_economy_qualification 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$pcexgcseabcnum1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- sem(NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,dat = datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1 
#mediate_plot(pcexgcseabcnum1~neighbourhood_economy_qualification+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcseabcnum1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsepsct1 M: pollution 
datNonCog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pollution < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsepsct1<- dat[dat$pollution > -4,] 

fitNonCog_corrected_FRC_1_pollution_pcexgcsepsct1<- sem(NonCog_corrected_FRC_1_pollution_pcexgcsepsct1,dat = datNonCog_corrected_FRC_1_pollution_pcexgcsepsct1) 
#summary(fitNonCog_corrected_FRC_1_pollution_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcsepsct1<- parameterEstimates(fitNonCog_corrected_FRC_1_pollution_pcexgcsepsct1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~pollution+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_pollution_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_pollution_pcexgcsepsct1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_pollution_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsepsct1",Model_est) 
ParamNonCog_corrected_FRC_1_pollution_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_pollution_pcexgcsepsct1) 
ParamfitNonCog_corrected_FRC_1_pollution_pcexgcsepsct1<- cbind(ParamNonCog_corrected_FRC_1_pollution_pcexgcsepsct1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_occupancyrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_occupancyrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsepsct1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_healthrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_healthrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsepsct1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_householdsize 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_householdsize+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsepsct1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_quality_populinhouseholds 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_quality_populinhouseholds+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsepsct1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsepsct1 M: neighbourhood_economy_qualification 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$pcexgcsepsct1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- sem(NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,dat = datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1 
#mediate_plot(pcexgcsepsct1~neighbourhood_economy_qualification+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsepsct1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsegrdm1 M: pollution 
datNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pollution < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- dat[dat$pollution > -4,] 

fitNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- sem(NonCog_corrected_FRC_1_pollution_pcexgcsegrdm1,dat = datNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1) 
#summary(fitNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~pollution+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsegrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1) 
ParamfitNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1<- cbind(ParamNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_occupancyrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_occupancyrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsegrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_healthrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_healthrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsegrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_householdsize 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_householdsize+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsegrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_quality_populinhouseholds 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_quality_populinhouseholds+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsegrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsegrdm1 M: neighbourhood_economy_qualification 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$pcexgcsegrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1 
#mediate_plot(pcexgcsegrdm1~neighbourhood_economy_qualification+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsegrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexothergrdm1 M: pollution 
datNonCog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datNonCog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pollution < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexothergrdm1<- dat[dat$pollution > -4,] 

fitNonCog_corrected_FRC_1_pollution_pcexothergrdm1<- sem(NonCog_corrected_FRC_1_pollution_pcexothergrdm1,dat = datNonCog_corrected_FRC_1_pollution_pcexothergrdm1) 
#summary(fitNonCog_corrected_FRC_1_pollution_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_pollution_pcexothergrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_pollution_pcexothergrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_pollution_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~pollution+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_pollution_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_pollution_pcexothergrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_pollution_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="pollution",EduAch="pcexothergrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_pollution_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_pollution_pcexothergrdm1) 
ParamfitNonCog_corrected_FRC_1_pollution_pcexothergrdm1<- cbind(ParamNonCog_corrected_FRC_1_pollution_pcexothergrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_occupancyrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_occupancyrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexothergrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_healthrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_healthrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexothergrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_householdsize 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_householdsize+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexothergrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_quality_populinhouseholds 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_quality_populinhouseholds+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexothergrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexothergrdm1 M: neighbourhood_economy_qualification 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$pcexothergrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$pcexothergrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1 
#mediate_plot(pcexothergrdm1~neighbourhood_economy_qualification+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexothergrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexadvgrdm1 M: pollution 
datNonCog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datNonCog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pollution < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexadvgrdm1<- dat[dat$pollution > -4,] 

fitNonCog_corrected_FRC_1_pollution_pcexadvgrdm1<- sem(NonCog_corrected_FRC_1_pollution_pcexadvgrdm1,dat = datNonCog_corrected_FRC_1_pollution_pcexadvgrdm1) 
#summary(fitNonCog_corrected_FRC_1_pollution_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_pollution_pcexadvgrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_pollution_pcexadvgrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_pollution_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~pollution+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_pollution_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_pollution_pcexadvgrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_pollution_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="pollution",EduAch="pcexadvgrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_pollution_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_pollution_pcexadvgrdm1) 
ParamfitNonCog_corrected_FRC_1_pollution_pcexadvgrdm1<- cbind(ParamNonCog_corrected_FRC_1_pollution_pcexadvgrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_occupancyrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_occupancyrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexadvgrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_healthrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_healthrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexadvgrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_householdsize 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_householdsize+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexadvgrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_quality_populinhouseholds 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_quality_populinhouseholds+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexadvgrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexadvgrdm1 M: neighbourhood_economy_qualification 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$pcexadvgrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1 
#mediate_plot(pcexadvgrdm1~neighbourhood_economy_qualification+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexadvgrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexallgrdm1 M: pollution 
datNonCog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datNonCog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pollution < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexallgrdm1<- dat[dat$pollution > -4,] 

fitNonCog_corrected_FRC_1_pollution_pcexallgrdm1<- sem(NonCog_corrected_FRC_1_pollution_pcexallgrdm1,dat = datNonCog_corrected_FRC_1_pollution_pcexallgrdm1) 
#summary(fitNonCog_corrected_FRC_1_pollution_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_pollution_pcexallgrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_pollution_pcexallgrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_pollution_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~pollution+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_pollution_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_pollution_pcexallgrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_pollution_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="pollution",EduAch="pcexallgrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_pollution_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_pollution_pcexallgrdm1) 
ParamfitNonCog_corrected_FRC_1_pollution_pcexallgrdm1<- cbind(ParamNonCog_corrected_FRC_1_pollution_pcexallgrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_occupancyrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_occupancyrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexallgrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_healthrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_healthrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexallgrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_householdsize 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_householdsize+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexallgrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_quality_populinhouseholds 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_quality_populinhouseholds+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexallgrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexallgrdm1 M: neighbourhood_economy_qualification 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$pcexallgrdm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$pcexallgrdm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- sem(NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,dat = datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1 
#mediate_plot(pcexallgrdm1~neighbourhood_economy_qualification+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexallgrdm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcseengm1 M: pollution 
datNonCog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pollution < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcseengm1<- dat[dat$pollution > -4,] 

fitNonCog_corrected_FRC_1_pollution_pcexgcseengm1<- sem(NonCog_corrected_FRC_1_pollution_pcexgcseengm1,dat = datNonCog_corrected_FRC_1_pollution_pcexgcseengm1) 
#summary(fitNonCog_corrected_FRC_1_pollution_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcseengm1<- parameterEstimates(fitNonCog_corrected_FRC_1_pollution_pcexgcseengm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~pollution+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_pollution_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_pollution_pcexgcseengm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_pollution_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcseengm1",Model_est) 
ParamNonCog_corrected_FRC_1_pollution_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_pollution_pcexgcseengm1) 
ParamfitNonCog_corrected_FRC_1_pollution_pcexgcseengm1<- cbind(ParamNonCog_corrected_FRC_1_pollution_pcexgcseengm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_occupancyrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_occupancyrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcseengm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_healthrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_healthrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcseengm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_householdsize 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_householdsize+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcseengm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_quality_populinhouseholds 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_quality_populinhouseholds+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcseengm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcseengm1 M: neighbourhood_economy_qualification 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$pcexgcseengm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$pcexgcseengm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- sem(NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,dat = datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1 
#mediate_plot(pcexgcseengm1~neighbourhood_economy_qualification+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcseengm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsescim1 M: pollution 
datNonCog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pollution < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsescim1<- dat[dat$pollution > -4,] 

fitNonCog_corrected_FRC_1_pollution_pcexgcsescim1<- sem(NonCog_corrected_FRC_1_pollution_pcexgcsescim1,dat = datNonCog_corrected_FRC_1_pollution_pcexgcsescim1) 
#summary(fitNonCog_corrected_FRC_1_pollution_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcsescim1<- parameterEstimates(fitNonCog_corrected_FRC_1_pollution_pcexgcsescim1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~pollution+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_pollution_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_pollution_pcexgcsescim1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_pollution_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsescim1",Model_est) 
ParamNonCog_corrected_FRC_1_pollution_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_pollution_pcexgcsescim1) 
ParamfitNonCog_corrected_FRC_1_pollution_pcexgcsescim1<- cbind(ParamNonCog_corrected_FRC_1_pollution_pcexgcsescim1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_occupancyrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_occupancyrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsescim1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_healthrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_healthrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsescim1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_householdsize 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_householdsize+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsescim1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_quality_populinhouseholds 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_quality_populinhouseholds+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsescim1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsescim1 M: neighbourhood_economy_qualification 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$pcexgcsescim1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$pcexgcsescim1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- sem(NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,dat = datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1 
#mediate_plot(pcexgcsescim1~neighbourhood_economy_qualification+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsescim1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsematm1 M: pollution 
datNonCog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pollution < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsematm1<- dat[dat$pollution > -4,] 

fitNonCog_corrected_FRC_1_pollution_pcexgcsematm1<- sem(NonCog_corrected_FRC_1_pollution_pcexgcsematm1,dat = datNonCog_corrected_FRC_1_pollution_pcexgcsematm1) 
#summary(fitNonCog_corrected_FRC_1_pollution_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcsematm1<- parameterEstimates(fitNonCog_corrected_FRC_1_pollution_pcexgcsematm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~pollution+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_pollution_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_pollution_pcexgcsematm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_pollution_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsematm1",Model_est) 
ParamNonCog_corrected_FRC_1_pollution_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_pollution_pcexgcsematm1) 
ParamfitNonCog_corrected_FRC_1_pollution_pcexgcsematm1<- cbind(ParamNonCog_corrected_FRC_1_pollution_pcexgcsematm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_occupancyrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_occupancyrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsematm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_healthrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_healthrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsematm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_householdsize 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_householdsize+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsematm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_quality_populinhouseholds 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_quality_populinhouseholds+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsematm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsematm1 M: neighbourhood_economy_qualification 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$pcexgcsematm1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$pcexgcsematm1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- sem(NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,dat = datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1 
#mediate_plot(pcexgcsematm1~neighbourhood_economy_qualification+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsematm1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsecorem1 M: pollution 
datNonCog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pollution < 4,] 
datNonCog_corrected_FRC_1_pollution_pcexgcsecorem1<- dat[dat$pollution > -4,] 

fitNonCog_corrected_FRC_1_pollution_pcexgcsecorem1<- sem(NonCog_corrected_FRC_1_pollution_pcexgcsecorem1,dat = datNonCog_corrected_FRC_1_pollution_pcexgcsecorem1) 
#summary(fitNonCog_corrected_FRC_1_pollution_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcsecorem1<- parameterEstimates(fitNonCog_corrected_FRC_1_pollution_pcexgcsecorem1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_pollution_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~pollution+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_pollution_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_pollution_pcexgcsecorem1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_pollution_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="pollution",EduAch="pcexgcsecorem1",Model_est) 
ParamNonCog_corrected_FRC_1_pollution_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_pollution_pcexgcsecorem1) 
ParamfitNonCog_corrected_FRC_1_pollution_pcexgcsecorem1<- cbind(ParamNonCog_corrected_FRC_1_pollution_pcexgcsecorem1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_occupancyrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_occupancyrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_occupancyrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_occupancyrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_occupancyrating",EduAch="pcexgcsecorem1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_healthrating 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_healthrating < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- dat[dat$neighbourhood_quality_healthrating > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_healthrating+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_healthrating",EduAch="pcexgcsecorem1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_householdsize 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$neighbourhood_quality_householdsize < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- dat[dat$neighbourhood_quality_householdsize > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_householdsize+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_householdsize",EduAch="pcexgcsecorem1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_quality_populinhouseholds 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$neighbourhood_quality_populinhouseholds < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- dat[dat$neighbourhood_quality_populinhouseholds > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- sem(NonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,dat = datNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_quality_populinhouseholds+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_quality_populinhouseholds",EduAch="pcexgcsecorem1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,Model_fit)

### GPS: NonCog_corrected_FRC_1 EA: pcexgcsecorem1 M: neighbourhood_economy_qualification 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$pcexgcsecorem1 > -4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$neighbourhood_economy_qualification < 4,] 
datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- dat[dat$neighbourhood_economy_qualification > -4,] 

fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- sem(NonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,dat = datNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1) 
#summary(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1, standardized=T, fit.measures=T, rsq=T) 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- parameterEstimates(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1, output = "data.frame") 
#output_fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1 
#mediate_plot(pcexgcsecorem1~neighbourhood_economy_qualification+NonCog_corrected_FRC_1,dat=dat) 
#semPaths(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1, "model","est", node.label.cex=5, edge.label.cex=1.5, fade=FALSE, sizeMan=10, label.cex = 1.5, curvePivot = TRUE, rotation = 2) 
#title("fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1") 
Model_est <- broom::tidy(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,conf.int = TRUE) 
Model_est1 <- cbind(GPS="NonCog_corrected_FRC_1",Mediator="neighbourhood_economy_qualification",EduAch="pcexgcsecorem1",Model_est) 
ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- Model_est1[c(7:9), c(1:3,6:15)] 
Model_fit <- broom::glance(fitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1) 
ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1<- cbind(ParamNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,Model_fit)

3 Conbine all moedel results together into a dataset

library(gtools)

## 
## Attaching package: 'gtools'

## The following object is masked from 'package:psych':
## 
##     logit

Model_est_neighbourhoods <- smartbind(
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsenum1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseabcnum1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsepsct1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsegrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexothergrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexadvgrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexallgrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcseengm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsescim1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsematm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_pollution_pcexgcsecorem1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,
  
  ParamfitEA3_Lee2018_no23andme_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsenum1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsenum1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsenum1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsenum1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsenum1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseabcnum1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseabcnum1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseabcnum1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseabcnum1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsepsct1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsepsct1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsepsct1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsepsct1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsegrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsegrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsegrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsegrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexothergrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexothergrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexothergrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexothergrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexothergrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexadvgrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexadvgrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexadvgrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexadvgrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexallgrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexallgrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexallgrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexallgrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexallgrdm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcseengm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcseengm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcseengm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcseengm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcseengm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsescim1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsescim1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsescim1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsescim1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsescim1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsematm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsematm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsematm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsematm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsematm1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_pollution_pcexgcsecorem1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_healthrating_pcexgcsecorem1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_householdsize_pcexgcsecorem1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,
  
  ParamfitIQ_Savage2018_corrected_FRCT1_neighbourhood_economy_qualification_pcexgcsecorem1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,
  
  Paramfitc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsenum1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseabcnum1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsepsct1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsegrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexothergrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexadvgrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexallgrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcseengm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsescim1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsematm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_pollution_pcexgcsecorem1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,
  
  Paramfitnc_chol_cog_noncog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,
  
  ParamfitCog_corrected_FRC_1_pollution_pcexgcsenum1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,
  
  ParamfitCog_corrected_FRC_1_pollution_pcexgcseabcnum1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,
  
  ParamfitCog_corrected_FRC_1_pollution_pcexgcsepsct1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,
  
  ParamfitCog_corrected_FRC_1_pollution_pcexgcsegrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,
  
  ParamfitCog_corrected_FRC_1_pollution_pcexothergrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,
  
  ParamfitCog_corrected_FRC_1_pollution_pcexadvgrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,
  
  ParamfitCog_corrected_FRC_1_pollution_pcexallgrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,
  
  ParamfitCog_corrected_FRC_1_pollution_pcexgcseengm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,
  
  ParamfitCog_corrected_FRC_1_pollution_pcexgcsescim1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,
  
  ParamfitCog_corrected_FRC_1_pollution_pcexgcsematm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,
  
  ParamfitCog_corrected_FRC_1_pollution_pcexgcsecorem1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,
  
  ParamfitCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1,
  
  ParamfitNonCog_corrected_FRC_1_pollution_pcexgcsenum1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsenum1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsenum1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsenum1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsenum1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsenum1,
  
  ParamfitNonCog_corrected_FRC_1_pollution_pcexgcseabcnum1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseabcnum1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseabcnum1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseabcnum1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseabcnum1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseabcnum1,
  
  ParamfitNonCog_corrected_FRC_1_pollution_pcexgcsepsct1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsepsct1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsepsct1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsepsct1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsepsct1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsepsct1,
  
  ParamfitNonCog_corrected_FRC_1_pollution_pcexgcsegrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsegrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsegrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsegrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsegrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsegrdm1,
  
  ParamfitNonCog_corrected_FRC_1_pollution_pcexothergrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexothergrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexothergrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexothergrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexothergrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexothergrdm1,
  
  ParamfitNonCog_corrected_FRC_1_pollution_pcexadvgrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexadvgrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexadvgrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexadvgrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexadvgrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexadvgrdm1,
  
  ParamfitNonCog_corrected_FRC_1_pollution_pcexallgrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexallgrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexallgrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexallgrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexallgrdm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexallgrdm1,
  
  ParamfitNonCog_corrected_FRC_1_pollution_pcexgcseengm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcseengm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcseengm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcseengm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcseengm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcseengm1,
  
  ParamfitNonCog_corrected_FRC_1_pollution_pcexgcsescim1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsescim1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsescim1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsescim1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsescim1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsescim1,
  
  ParamfitNonCog_corrected_FRC_1_pollution_pcexgcsematm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsematm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsematm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsematm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsematm1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsematm1,
  
  ParamfitNonCog_corrected_FRC_1_pollution_pcexgcsecorem1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_occupancyrating_pcexgcsecorem1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_healthrating_pcexgcsecorem1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_householdsize_pcexgcsecorem1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_quality_populinhouseholds_pcexgcsecorem1,
  
  ParamfitNonCog_corrected_FRC_1_neighbourhood_economy_qualification_pcexgcsecorem1)